1. Field of the Invention
The present invention relates to image processing based on a human vision model.
2. Description of the Related Art
Nowadays, the household penetration rate of digital cameras in the country exceeds 50% (2004 Consumer Behavior Survey (Cabinet Office)), and “photographing by a digital camera” is a very common activity.
Upon photographing a certain scene by a digital camera outdoors, the luminance range of a photographing target (scene) is often broader than a photographable luminance range. In this case, tone information of an object outside the photographable luminance range cannot be recorded, thus causing a so-called highlight-detail loss and shadow-detail loss. For example, when a person is photographed outdoors on a fine day while the exposure value is adjusted to that person, a highlight-detail loss occurs in an image region including sky and clouds of the background, and a shadow-detail loss occurs in an image region including the shade of trees. On the other hand, the human vision has a characteristic called “local adaptation” that switches the state of adaptation according to the lightness level of a viewing region, and allows to perceive lightness levels and colors, and one can perceive tones in both bright and dark places. For this reason, an impression upon directly viewing a photographing scene is often different from that upon viewing a photographed image of that scene.
As one of techniques that can solve such problem, a high-dynamic-range imaging (HDR) technique is known. The HDR technique roughly includes an HDR capture technique and HDR reproduction technique.
The HDR capture technique is used to expand a photographing dynamic range so as to prevent any highlight-detail loss and shadow-detail loss, and a method of compositing images photographed using a plurality of exposure values is known. An image acquired by this HDR capture technique will be referred to as an HDR image hereinafter.
The HDR reproduction technique is used to well reproduce an HDR image with a broad dynamic range by a display device or output device (to be referred to as an image output device hereinafter) with a narrow dynamic range and, for example, a method of compressing low-frequency components of an HDR image is known. In this manner, the HDR technique can eliminate a highlight-detail loss and shadow-detail loss by the capture technique used to expand the dynamic range and the corresponding reproduction technique.
In recent years, image correction using a human vision model is applied to the HDR reproduction technique. The human vision model is obtained by modeling the visual characteristics such as a color adaptation characteristic and luminance adaptation characteristic of a human, and can express how one views an actual scene using numerical values based on the state of adaptation of the vision.
FIG. 1 is a view for explaining an overview of image correction using the human vision model in the HDR reproduction technique.
An HDR image 10 before correction is input, and data (a state of adaptation 11) indicating the lightness levels and colors of an actual scene perceived by a person are calculated based on the human vision model. Then, image correction (correction for adaptation) 12 is applied to reproduce the state of adaptation 11 by an output device 13 as best as possible. In this manner, the colors and tones of an actual scene are faithfully reproduced.
As the human vision model used in the HDR reproduction technique, a human vision model, which switches the state of adaptation according to the lightness levels and colors of a viewing object and considers the local adaptation characteristic (to be referred to as a local adaptation model hereinafter), as typified by the iCAM (Kuang, J., Johnson, G. M., Fairchild M. D. “iCAM06: a refined image appearance model for HDR image rendering” Journal of Visual Communication, 2007), has been proposed. The local adaptation model calculates a state of adaptation according to a viewing region by applying low-pass filter processing to an image before correction. That is, the local adaptation model calculates states of local adaptation by applying the low-pass filter processing to an image.
However, it is pointed out that the low-pass filter processing excessively reflects the locality of adaptation (Yamaguchi, H., Fairchild M. D. “A Study of Simultaneous Lightness Perception for Stimuli with Multiple Illumination Levels” 12th CIC, 22-29). That is, the low-pass filter processing does not always precisely simulate how one views an actual scene, and preferred reproduction may sometimes not be obtained. In other words, a novel method which calculates the states of local adaptation of the vision more accurately at the time of photographing an image is required.